{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Project to identify arbitrage opportunities between two stock exchanges trading the same stock. The algorithm searches for the possibility of a mismatch and trades on it. Next to that, it takes into account certain limits, which is set to a max position of 250 to prevent massive losses if the algorithm malfunctions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas and numpy for analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set Jupyter to render directly to the screen\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def read_data(filename):\n",
    "    '''\n",
    "    This reads the .csv stored at the 'filename' location and returns a DataFrame\n",
    "    with two-level columns. The first level column contains the Exchange and the \n",
    "    second contains the type of market data, e.g. bid/ask, price/volume.\n",
    "    '''\n",
    "    df = pd.read_csv(filename, index_col=0)\n",
    "    df.columns = [df.columns.str[-7:], df.columns.str[:-8]]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Read the data for one of the stocks\n",
    "filename = 'HWG.csv'\n",
    "market_data = read_data(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">I-XCHNG</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Z-XCHNG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>BidPrice</th>\n",
       "      <th>BidVolume</th>\n",
       "      <th>AskPrice</th>\n",
       "      <th>AskVolume</th>\n",
       "      <th>BidPrice</th>\n",
       "      <th>BidVolume</th>\n",
       "      <th>AskPrice</th>\n",
       "      <th>AskVolume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:05:00</th>\n",
       "      <td>114.25</td>\n",
       "      <td>120.0</td>\n",
       "      <td>114.55</td>\n",
       "      <td>120.0</td>\n",
       "      <td>113.95</td>\n",
       "      <td>40.0</td>\n",
       "      <td>114.85</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:10:00</th>\n",
       "      <td>114.05</td>\n",
       "      <td>119.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>102.0</td>\n",
       "      <td>113.65</td>\n",
       "      <td>42.0</td>\n",
       "      <td>114.60</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:15:00</th>\n",
       "      <td>114.05</td>\n",
       "      <td>119.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>105.0</td>\n",
       "      <td>113.30</td>\n",
       "      <td>41.0</td>\n",
       "      <td>114.35</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:20:00</th>\n",
       "      <td>114.00</td>\n",
       "      <td>116.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>124.0</td>\n",
       "      <td>113.50</td>\n",
       "      <td>40.0</td>\n",
       "      <td>114.55</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:25:00</th>\n",
       "      <td>114.00</td>\n",
       "      <td>118.0</td>\n",
       "      <td>114.35</td>\n",
       "      <td>133.0</td>\n",
       "      <td>113.45</td>\n",
       "      <td>43.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     I-XCHNG                               Z-XCHNG            \\\n",
       "                    BidPrice BidVolume AskPrice AskVolume BidPrice BidVolume   \n",
       "2018-01-01 00:05:00   114.25     120.0   114.55     120.0   113.95      40.0   \n",
       "2018-01-01 00:10:00   114.05     119.0   114.40     102.0   113.65      42.0   \n",
       "2018-01-01 00:15:00   114.05     119.0   114.40     105.0   113.30      41.0   \n",
       "2018-01-01 00:20:00   114.00     116.0   114.40     124.0   113.50      40.0   \n",
       "2018-01-01 00:25:00   114.00     118.0   114.35     133.0   113.45      43.0   \n",
       "\n",
       "                                        \n",
       "                    AskPrice AskVolume  \n",
       "2018-01-01 00:05:00   114.85      40.0  \n",
       "2018-01-01 00:10:00   114.60      36.0  \n",
       "2018-01-01 00:15:00   114.35      35.0  \n",
       "2018-01-01 00:20:00   114.55      37.0  \n",
       "2018-01-01 00:25:00   114.40      33.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualization of the columns in the DataFrame\n",
    "market_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the first 250 rows\n",
    "market_data_250 = market_data.iloc[:250]\n",
    "\n",
    "# Set figsize of plot\n",
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "# Create a plot showing the bid and ask prices on different exchanges\n",
    "def Plot_Bid_Ask(stock1 = 'I-XCHNG', stock2 = 'Z-XCHNG'):\n",
    "    plt.plot(market_data_250.index, market_data_250[stock1, 'BidPrice'])\n",
    "    plt.plot(market_data_250.index, market_data_250[stock2, 'AskPrice'])\n",
    "    plt.xticks([])\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Price')\n",
    "    plt.title('Bid Price on '  + stock1 + ' vs Ask Price on ' + stock2)\n",
    "    plt.legend([stock1 + ' BidPrice', stock2 + ' AskPrice'])\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Note arbitrage possible in case the BidPrice is higher than the AskPrice.\n",
    "Plot_Bid_Ask()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">I-XCHNG</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Z-XCHNG</th>\n",
       "      <th>I-Bid-Z-Ask-Spread</th>\n",
       "      <th>Z-Bid-I-Ask-Spread</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>BidPrice</th>\n",
       "      <th>BidVolume</th>\n",
       "      <th>AskPrice</th>\n",
       "      <th>AskVolume</th>\n",
       "      <th>BidPrice</th>\n",
       "      <th>BidVolume</th>\n",
       "      <th>AskPrice</th>\n",
       "      <th>AskVolume</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:05:00</th>\n",
       "      <td>114.25</td>\n",
       "      <td>120.0</td>\n",
       "      <td>114.55</td>\n",
       "      <td>120.0</td>\n",
       "      <td>113.95</td>\n",
       "      <td>40.0</td>\n",
       "      <td>114.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:10:00</th>\n",
       "      <td>114.05</td>\n",
       "      <td>119.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>102.0</td>\n",
       "      <td>113.65</td>\n",
       "      <td>42.0</td>\n",
       "      <td>114.60</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:15:00</th>\n",
       "      <td>114.05</td>\n",
       "      <td>119.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>105.0</td>\n",
       "      <td>113.30</td>\n",
       "      <td>41.0</td>\n",
       "      <td>114.35</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:20:00</th>\n",
       "      <td>114.00</td>\n",
       "      <td>116.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>124.0</td>\n",
       "      <td>113.50</td>\n",
       "      <td>40.0</td>\n",
       "      <td>114.55</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:25:00</th>\n",
       "      <td>114.00</td>\n",
       "      <td>118.0</td>\n",
       "      <td>114.35</td>\n",
       "      <td>133.0</td>\n",
       "      <td>113.45</td>\n",
       "      <td>43.0</td>\n",
       "      <td>114.40</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     I-XCHNG                               Z-XCHNG            \\\n",
       "                    BidPrice BidVolume AskPrice AskVolume BidPrice BidVolume   \n",
       "2018-01-01 00:05:00   114.25     120.0   114.55     120.0   113.95      40.0   \n",
       "2018-01-01 00:10:00   114.05     119.0   114.40     102.0   113.65      42.0   \n",
       "2018-01-01 00:15:00   114.05     119.0   114.40     105.0   113.30      41.0   \n",
       "2018-01-01 00:20:00   114.00     116.0   114.40     124.0   113.50      40.0   \n",
       "2018-01-01 00:25:00   114.00     118.0   114.35     133.0   113.45      43.0   \n",
       "\n",
       "                                       I-Bid-Z-Ask-Spread Z-Bid-I-Ask-Spread  \n",
       "                    AskPrice AskVolume                                        \n",
       "2018-01-01 00:05:00   114.85      40.0              -0.60              -0.60  \n",
       "2018-01-01 00:10:00   114.60      36.0              -0.55              -0.75  \n",
       "2018-01-01 00:15:00   114.35      35.0              -0.30              -1.10  \n",
       "2018-01-01 00:20:00   114.55      37.0              -0.55              -0.90  \n",
       "2018-01-01 00:25:00   114.40      33.0              -0.40              -0.90  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add columns to DataFrame\n",
    "market_data['I-Bid-Z-Ask-Spread'] = market_data['I-XCHNG', 'BidPrice'] - market_data['Z-XCHNG', 'AskPrice']\n",
    "market_data['Z-Bid-I-Ask-Spread'] = market_data['Z-XCHNG', 'BidPrice'] - market_data['I-XCHNG', 'AskPrice']\n",
    "market_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">I-XCHNG</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Z-XCHNG</th>\n",
       "      <th>I-Bid-Z-Ask-Spread</th>\n",
       "      <th>Z-Bid-I-Ask-Spread</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>BidPrice</th>\n",
       "      <th>BidVolume</th>\n",
       "      <th>AskPrice</th>\n",
       "      <th>AskVolume</th>\n",
       "      <th>BidPrice</th>\n",
       "      <th>BidVolume</th>\n",
       "      <th>AskPrice</th>\n",
       "      <th>AskVolume</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:30:00</th>\n",
       "      <td>113.60</td>\n",
       "      <td>119.0</td>\n",
       "      <td>113.90</td>\n",
       "      <td>98.0</td>\n",
       "      <td>114.05</td>\n",
       "      <td>34.0</td>\n",
       "      <td>114.95</td>\n",
       "      <td>39.0</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:35:00</th>\n",
       "      <td>113.70</td>\n",
       "      <td>108.0</td>\n",
       "      <td>114.00</td>\n",
       "      <td>103.0</td>\n",
       "      <td>114.20</td>\n",
       "      <td>33.0</td>\n",
       "      <td>115.05</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:05:00</th>\n",
       "      <td>113.80</td>\n",
       "      <td>88.0</td>\n",
       "      <td>114.10</td>\n",
       "      <td>88.0</td>\n",
       "      <td>112.90</td>\n",
       "      <td>24.0</td>\n",
       "      <td>113.75</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 15:05:00</th>\n",
       "      <td>113.55</td>\n",
       "      <td>130.0</td>\n",
       "      <td>113.85</td>\n",
       "      <td>112.0</td>\n",
       "      <td>112.70</td>\n",
       "      <td>37.0</td>\n",
       "      <td>113.50</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-1.15</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-01 15:10:00</th>\n",
       "      <td>113.55</td>\n",
       "      <td>129.0</td>\n",
       "      <td>113.85</td>\n",
       "      <td>113.0</td>\n",
       "      <td>112.55</td>\n",
       "      <td>38.0</td>\n",
       "      <td>113.35</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-1.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     I-XCHNG                               Z-XCHNG            \\\n",
       "                    BidPrice BidVolume AskPrice AskVolume BidPrice BidVolume   \n",
       "2018-01-01 04:30:00   113.60     119.0   113.90      98.0   114.05      34.0   \n",
       "2018-01-01 04:35:00   113.70     108.0   114.00     103.0   114.20      33.0   \n",
       "2018-01-01 09:05:00   113.80      88.0   114.10      88.0   112.90      24.0   \n",
       "2018-01-01 15:05:00   113.55     130.0   113.85     112.0   112.70      37.0   \n",
       "2018-01-01 15:10:00   113.55     129.0   113.85     113.0   112.55      38.0   \n",
       "\n",
       "                                       I-Bid-Z-Ask-Spread Z-Bid-I-Ask-Spread  \n",
       "                    AskPrice AskVolume                                        \n",
       "2018-01-01 04:30:00   114.95      39.0              -1.35               0.15  \n",
       "2018-01-01 04:35:00   115.05      35.0              -1.35               0.20  \n",
       "2018-01-01 09:05:00   113.75      31.0               0.05              -1.20  \n",
       "2018-01-01 15:05:00   113.50      39.0               0.05              -1.15  \n",
       "2018-01-01 15:10:00   113.35      39.0               0.20              -1.30  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new DataFrame containing all arbitrage opportunities for comparison\n",
    "arbitrage = market_data.loc[(market_data['I-Bid-Z-Ask-Spread'] > 0) | (market_data['Z-Bid-I-Ask-Spread'] > 0)]\n",
    "arbitrage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position-I-XCHNG</th>\n",
       "      <th>Position-Z-XCHNG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:30:00</th>\n",
       "      <td>34.0</td>\n",
       "      <td>-34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:35:00</th>\n",
       "      <td>67.0</td>\n",
       "      <td>-67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:05:00</th>\n",
       "      <td>-64.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 15:05:00</th>\n",
       "      <td>-70.0</td>\n",
       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-01 15:10:00</th>\n",
       "      <td>-78.0</td>\n",
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      ],
      "text/plain": [
       "                     Position-I-XCHNG  Position-Z-XCHNG\n",
       "Timestamp                                              \n",
       "2018-01-01 04:30:00              34.0             -34.0\n",
       "2018-01-01 04:35:00              67.0             -67.0\n",
       "2018-01-01 09:05:00             -64.0              64.0\n",
       "2018-01-01 15:05:00             -70.0              70.0\n",
       "2018-01-01 15:10:00             -78.0              78.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design arbitrage strategy that gives all positions\n",
    "\n",
    "positions = {'Timestamp': [],\n",
    "             'Position-I-XCHNG': [],\n",
    "             'Position-Z-XCHNG': []}\n",
    "\n",
    "current_position = 0\n",
    "\n",
    "for time, mkt_data_at_time in market_data.iterrows():\n",
    "\n",
    "    if mkt_data_at_time['I-Bid-Z-Ask-Spread', ''] > 0:\n",
    "        buy = min(mkt_data_at_time['I-XCHNG', 'BidVolume'],\n",
    "                  mkt_data_at_time['Z-XCHNG', 'AskVolume'], (250 - current_position))\n",
    "        spread = mkt_data_at_time['I-Bid-Z-Ask-Spread']\n",
    "        positions['Timestamp'].append(time)\n",
    "        positions['Position-I-XCHNG'].append(- buy - current_position)\n",
    "        positions['Position-Z-XCHNG'].append(+ buy + current_position)\n",
    "        current_position = buy\n",
    "\n",
    "    elif mkt_data_at_time['Z-Bid-I-Ask-Spread', ''] > 0:\n",
    "        buy = min(mkt_data_at_time['Z-XCHNG', 'BidVolume'],\n",
    "                  mkt_data_at_time['I-XCHNG', 'AskVolume'], (250 - current_position))\n",
    "        spread = mkt_data_at_time['Z-Bid-I-Ask-Spread']\n",
    "        positions['Timestamp'].append(time)\n",
    "        positions['Position-I-XCHNG'].append(+ buy + current_position)\n",
    "        positions['Position-Z-XCHNG'].append(- buy - current_position)\n",
    "        current_position = buy\n",
    "\n",
    "positions = pd.DataFrame(positions).set_index('Timestamp')\n",
    "\n",
    "positions.head()"
   ]
  }
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